1. Field of the Invention
This invention relates, generally, to pelvic organ prolapse. More specifically, it relates to a methodology for analyzing medical images to facilitate prediction and/or diagnosis of pelvic organ prolapse.
2. Brief Description of the Prior Art
Pelvic organ prolapse (POP) is a serious health condition that affects about 30-50% of women in the U.S. [1] and has direct costs of approximately $1 billion/year. POP symptoms are generally nonspecific, and it occurs when a pelvic organ such as bladder, uterus, small bowel and rectum drops from its normal position and pushes against the vaginal walls. It can cause significant problems such as a vaginal bulge, bowel and bladder incontinence, and sexual dysfunction. Approximately 75% of women reported an effect on their quality of life due to POP [51].
The International Continence Society (ICS) recommends the use of the Pelvic Organ Prolapse Quantification (POP-Q) system [2] for diagnosis of POP through clinical examination. POP-Q is currently considered the gold standard for assessing POP. However, clinical examination has been found to be inadequate in about 41% of the cases. Groenendijk et al. [5] found that the diagnostic accuracy of clinical examination for POP was 0.42. Therefore, there is a major need to improve the diagnosis of POP.
Dynamic magnetic resonance imaging (MRI) of the pelvic floor has become increasingly popular in assessing POP cases that may not be evident during clinical examination (due to the inaccuracies of clinical examination). Dynamic MRI complements clinical examination by providing a global assessment of the movements and interactions of pelvic floor organs, while avoiding the use of ionizing radiation [6]. During the analysis of dynamic MRI, anatomical landmarks/reference points are manually identified on pelvic bone structures (pubic bone, sacral promontory, and coccyx) on the midsagittal plane to determine reference lines and measurements for grading and determining the stages of POP (see FIG. 1A) [7]. Dynamic MRI can be particularly useful in analyzing multiple compartment prolapse cases. However, the manual identification of these measurements is a time-consuming and subjective procedure. Based on these points, reference lines are drawn to measure and define the severity of POP as shown in FIG. 1B [7].
More specifically, the most commonly used reference lines for measuring POP are the pubococcygeal line (PCL), and the mid-pubic line (MPL). The PCL is determined by the inferior border of the pubic bone and the last visible coccygeal joint, while the MPL is a midsagittal long axis of the symphysis pubis and extends through the level of the vaginal hymen [52, 53]. Distances are then measured from PCL and MPL to the furthest most descent of the pelvic organs including the bladder neck, cervix, and anorectal junction on the images when the patient is at rest and under maximum pelvic strain. Based on these distances, the severity of prolapse can be graded as mild, moderate, or severe [54].
Although there are commonly used reference lines, as discussed, there is no standardized system for evaluating MRI measurements for POP, and previous studies that analyze the correlation between clinical and MRI measurements for POP diagnosis have been limited. Moreover, these studies used few features (variables) based on commonly used MRI reference lines such as PCL and MPL. Current studies have only analyzed small sample sizes resulting in limited and non-comparable data [8, 9]. This has restricted the correlation analysis of MRI measurements with clinical and surgical outcomes/information, as well as restricting the validation of newly proposed reference lines. A functional correlation between MRI measurements and POP has been found for two types of prolapse: anterior and apical. However, for the posterior type of prolapse, no correlation has been found, making this the most difficult type of prolapse to diagnose.
As noted, dynamic MRI is a promising complementary diagnostic tool for POP but appropriate validation has been limited. Automating the identification of reference points, lines, and measurements is expected to facilitate the high throughput analysis of images and improve the evaluation of POP. To this aim, pelvic bone structures need to be segmented and their corresponding reference points identified automatically. However, segmentation of bone structures on MRI is a challenging task since the pixel intensities of bones can be very similar to the pixel intensities of other structures such as soft tissue, fat and air. For this reason, challenges remain for bone segmentation on MRI; these challenges have not been overcome by the conventional art. Therefore, there is a major need to investigate the correlation between clinical and MRI-based features as well as to test new MRI-based features that can potentially improve the prediction of prolapse, particularly for posterior prolapse.
Previous methods for segmentation on MRI include region growing approaches [10, 11], active shape models [12], general deformable models [13-15], clustering methods [16], and graph-based approaches [17]. Lorigo et al. [18] segmented the knee bone using texture-based geodesic active contours. Fripp et al. [12] segmented the knee bone using 3D active shape models initialized by affine registration to an atlas. Shape models [19], normalized cuts [20] and graph cut [21-23] have been used to segment the femur and hip bones, spinal, and femoral head, respectively. Schmid et al. [19] presented a technique based on physically-based deformable models and prior shape knowledge to segment the femur and hip bones on MRI. Yin et al. [24] used graph cuts for knee-joint bone and cartilage segmentation.
Recently, segmentation techniques based on statistical classification have been used for bone segmentation on MRI [25, 26]. These techniques group pixels or voxels based on distinguished features such as intensities, gradients and texture. Simple intensity-based features do not provide successful segmentation results because different tissues have overlapping image intensity values. Bourgeat et al. [25] used Gabor filter features extracted from the phase of MR signal to improve texture discrimination in bone segmentation. van Ginneken et al. [26] combined texture based classification with the anatomically valid shape information of the chest structure to constrain the segmentation. Although these methods present promising results, the main drawbacks are high computation time, initialization sensitivity, definition of many parameters, and lack of leak detection processes.
Various approaches have been proposed for the automated localization of multiple organs such as heart, liver, spleen, lungs, kidneys and bladder on medical images using geometric methods, statistical atlas-based techniques, and supervised methods [35-42]. Among supervised methods, there has been an increasing interest in regression-based approaches for anatomical structure localization, since organs and tissues in the human body have known relative arrangement. Zheng et al. [43]proposed an approach called marginal space learning (MSL) that uses a set of classifiers based on probabilistic boosting tree (PBT) to predict the position, position orientation and full 3D pose. In [44], the authors further expanded this idea to non-rigid marginal space learning using statistical shape models. Zhou et al. [45] introduced an approach based on boosting ridge regression to detect and localize the left ventricle (LV) in cardiac ultrasound 2D images. Criminisi et al. [46]proposed regression forests to predict the location of multiple anatomical structures in CT scans. Cuingnet et al. [47] presented an improved regression forest by adding a refinement step to the detection process to find kidneys in CT scans. These methods use the difference in mean intensities to locate the bounding boxes of the anatomical structures on the images. However, considering only intensity levels is not sufficient for the localization of anatomical structures such as bones on MRI.
Further attempts have been made to overcome the foregoing drawbacks of the conventional art. Examples include [64]-[68]. However, each of the foregoing disclosures/technologies includes any one or more of the drawbacks previously discussed, and they may not be fully effective and/or not completely accurate.
One or more of the current inventors' previous publications include the following:    S. Onal, S. Lai-Yuen, P. Bao, A. Weltzenfeld, K. Greene, R. Kedar, S. Hart, “Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI”, Int Urogynecol J, 25(6):767-773 (published Jan. 16, 2014).    Onal, S., Lai-Yuen, S., Bao, P., Weitzenfeld, A., Stuart, H. “MRI based Segmentation of Pelvic Bone for Evaluation of Pelvic Organ Prolapse”. IEEE Journal of Biomedical and Health Informatics. (In Press)    Onal, S., Lai-Yuen, S., Bao, P., Weitzenfeld, A., Stuart, H. “Image based measurements for evaluation of pelvic organ prolapse”. Journal of Biomedical Science and Engineering (JBiSE), 2013. 6 (1): p 45-55.    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Hart, S. (2013) “Pubic Bone Segmentation for Diagnosis of Pelvic Organ Prolapse”. 6th Annual College of Engineering Research Day, November 6, Tampa, Fla.    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Greene, K., Hart, S. (2013) “Image Based Measurements for Evaluation of Pelvic Organ Prolapse”. AUGS 34th. Annual Scientific Meeting, October 16-19, Las Vegas, Nev.    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Hart, S. (2013) “Combined Supervised and Unsupervised Segmentation of Pubic Bone for Diagnosis of Pelvic Organ Prolapse” INFORMS Annual Meeting, October 6-9, Minneapolis, Minn.    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Greene, K., Hart, S. (2013) “Image Based Measurements for Evaluation of Pelvic Organ Prolapse”. 38th. Annual Meeting—International Urogynecological Association, May 28-June 1, Dublin, Ireland    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Hart, S. (2013) “MRI-based Segmentation of Pubic Bone for Evaluation of Pelvic Organ Prolapse”. Graduate Student and Postdoctoral Scholar Research Symposium, March 25, Tampa, Fla.    Onal, S., Lai-Yuen, S, Bao, P., Weitzenfeld, A., Hogue, D., Hart, S. (28 Nov. 2014) “Quantitative assessment of new MRI-based measurement to differentiate low and high stages of pelvic organ prolapse using support vector machines”. Int Urogynecol J, 26:707-713.
Accordingly, what is needed is an automated analysis of medical images to facilitate rapid and accurate diagnosis of pelvic organ prolapse. However, in view of the art considered as a whole at the time the present invention was made, it was not obvious to those of ordinary skill in the field of this invention how the shortcomings of the prior art could be overcome.
While certain aspects of conventional technologies have been discussed to facilitate disclosure of the invention, Applicants in no way disclaim these technical aspects, and it is contemplated that the claimed invention may encompass one or more of the conventional technical aspects discussed herein.
The present invention may address one or more of the problems and deficiencies of the prior art discussed above. However, it is contemplated that the invention may prove useful in addressing other problems and deficiencies in a number of technical areas. Therefore, the claimed invention should not necessarily be construed as limited to addressing any of the particular problems or deficiencies discussed herein.
In this specification, where a document, act or item of knowledge is referred to or discussed, this reference or discussion is not an admission that the document, act or item of knowledge or any combination thereof was at the priority date, publicly available, known to the public, part of common general knowledge, or otherwise constitutes prior art under the applicable statutory provisions; or is known to be relevant to an attempt to solve any problem with which this specification is concerned.